International Journal of Accounting research.pdf

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DIGITAL TRANSFORMATION AND BANK’S PERFORMANCE: CASE OF
COMMERCIAL BANKS IN VIETNAM
Abstract:
Digital transformation within the banking sector stands as a pivotal development in today's rapidly
evolving technological landscape. This research specifically delves into the multifaceted nature of
digital transformation in the Vietnamese banking sector, aiming to dissect its impact on bank
performance and elucidate the nuances of its integration into diverse dimensions of banking
operations. To meet the objectives, the research employed regression models to analyze data
extracted from various banks, focusing particularly on the areas of strategic transformation,
business transformation, and management transformation. These dimensions were gauged by
assessing the frequency of digital technology-related terms in annual reports, digital channels,
products, R&D innovation, and organizational restructuring metrics. Findings reveal a significant
association between strategic transformation, gauged by the prevalence of digital terms in annual
reports, and enhanced bank performance. In contrast, while the potential benefits of business and
management transformations were evident, their relationships with bank performance did not
register as significant in the regression analysis. In conclusion, the digital transformation journey,
while promising, holds complexities, dependent on a bank's specific context and the dimensions it
emphasizes during its digital integration. The research underscores the essentiality of strategic
alignment of digital initiatives with core banking functions to ensure tangible performance
improvements, especially in the Vietnamese context. The insights provided by this study serve as
valuable pointers for investors, managers, and government agencies, shaping their strategies within
the digital banking paradigm.
Keywords: Digital Transformation, Banks, Vietnam
JEL Codes: G21, O33
1. Introduction
The banking industry is undergoing a profound metamorphosis, largely propelled by digital
transformation. Central to this transformation is the necessity for banks to maintain agility in a
rapidly evolving market landscape, especially in the face of disruptive and dynamic changes.
Digital transformation in banks has not only affected business operations and customer interactions
but has also significantly influenced employee engagement and the overall work environment. The
need to secure employee commitment during such transformations is crucial, as shifts can deeply
impact their psychological well-being (Winasis et al., 2020).
Digital transformation's effect on banking has not been solely propelled by technological
advancements but also external factors. The COVID-19 pandemic acted as a catalyst, pushing
businesses, especially those in the financial sector, to modify their operations. This shift made
working from home commonplace, necessitating the adoption of digital technologies and altering
traditional business models (Stalmachova et al., 2021). These researchers also stress the
importance of measuring these transformations using tools like the Business Model Canvas and
the Balanced Scorecard to ensure the long-term sustainability of banking.

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This sentiment of ongoing transformation and adaptation resonates in the European context as
well. Filotto et al. (2021) found that user-friendliness and economic advantages were primary
determinants for the early stages of digital adoption in banking. However, for sustained usage and
loyalty, banks need to emphasize security policies and guarantees. Another key insight is that
accessibility and compatibility might not be as crucial as previously assumed. In the North
American context, Pramanik et al. (2019) highlighted the lack of a unified definition for digital
transformation, given the myriad of interpretations across stakeholders. However, by analyzing
narratives from large financial institutions, they underscored the importance of understanding the
essence of transformation when institutions embrace digital technologies. Their research proposed
a Digital Transformation Maturity Model (DTMM) based on these findings, aiming to serve as a
maturity guide for other financial institutions contemplating similar digital transitions.
The Asian context, particularly Vietnam, also provides a compelling backdrop for understanding
digital transformation in banking. Do et al. (2022) analyzed the impact of digital transformation
on Vietnamese commercial banks' performance and found a positive correlation, particularly
emphasizing the role bank size plays in influencing this relationship. Another Vietnamese
perspective highlighted the challenges faced by banks during the fourth industrial revolution and
the effects of the US-China trade war, underlining the necessity for better risk databases for bank
sustainability (Anh et al., 2021). Additionally, Le and Pham (2022) substantiated that the
development of Information and Communication Technology (ICT) in banks positively affects
profitability, especially during the transformation from traditional to digital systems.
In Vietnam, a burgeoning economy with a rapidly urbanizing population, digital transformation in
the banking sector has taken center stage. As with many developing nations, the financial
landscape in Vietnam is characterized by its dichotomy: on one side are the traditional banks,
grounded in legacy systems and methodologies, and on the other are modern financial enterprises,
infused with technology and innovation, ready to capitalize on the digital age's benefits. However,
the impetus is now on traditional Vietnamese banks to embark on digital journeys lest they risk
obsolescence or losing out to nimbler, tech-savvy competitors.
Yet, questions arise. Does the fervent embrace of digital technologies unequivocally translate to
better bank performance in Vietnam? Is the sizable investment in technology yielding the
anticipated dividends in the form of efficiency, customer satisfaction, and, ultimately, profitability?
While China, a neighboring economic powerhouse, has seen its banks' digital transformation
endeavors underpinned by state-driven initiatives and substantial technological investments (Xie
& Wang, 2023), the narrative in Vietnam is still unfolding.
This research aims to delve into the Vietnamese context, investigating the tangible effects of digital
transformation on bank performance within the nation. Despite the palpable buzz surrounding the
topic and the clear strategic shifts by Vietnamese banks toward digital paradigms, empirical studies
scrutinizing the actual impacts on performance remain scant. Given the unique socio-economic
and regulatory backdrop of Vietnam, understanding the digital transformation's nuances in its
banking sector becomes paramount.

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In contributing to this domain, this research offers a threefold value. First, it furnishes a
comprehensive understanding of the current state of digital transformation in Vietnam's banking
sector, contextualized within its unique challenges and opportunities. Second, the research equips
stakeholders—ranging from bank executives to policymakers—with empirical evidence, aiding
informed decision-making about technological investments and strategic pivots. Lastly, by
unveiling the nexus between digital transformation and bank performance in Vietnam, the study
augments the global discourse on the topic, enriching the understanding of digital transformation's
tangible impacts in varied economic landscapes.
2. Literature Review
2.1 Concepts & Measurements
2.1.1 Digital transformation in banks
The term 'digital transformation' in the context of banking signifies a comprehensive restructuring
and modernization of banking systems and operations through the incorporation of emerging
digital technologies. It goes beyond mere technological implementation; it encapsulates a shift in
mindset, culture, and core operational procedures. This transformation is instigated by
advancements in several digital domains. Enhanced connectivity of systems, for instance, allows
for more immediate and expansive data exchanges, while leaps in computing power facilitate more
robust and complex operations at diminished costs. Moreover, the vast reservoirs of newly
generated and usable data have revolutionized decision-making processes, risk assessments, and
customer interactions (Feyen et al., 2021).
A pivotal outcome of the digital transformation has been the proliferation of innovative business
models. The financial landscape, traditionally dominated by established institutions, has seen an
influx of new entrants. These entities, leveraging technological capabilities, have introduced
models that deconstruct traditional banking services. This 'unbundling' allows consumers to tailor
their financial engagements, selecting specific services that cater to their unique needs (Feyen et
al., 2021). However, it's not just new entrants that are pivotal in reshaping the market; established
banks are undergoing internal revolutions. For instance, the intensive application of Information
Technology has been identified as a strategic tool that bridges information asymmetries, enhancing
both productivity and market positioning (Koetter & Noth, 2013).
While the transformation is broad-ranging, specific technological realms have been particularly
influential. Areas like internet technology, artificial intelligence, blockchain, cloud computing, and
big data have emerged as cornerstones of the modern banking paradigm. Cheng and Qu (2020)
research provides an insightful lens into this, especially within the Chinese banking sector. They
found that the adoption of such technologies not only accelerates operational efficiencies but
significantly bolsters risk management frameworks. In an environment where credit risks are
paramount, fintech's role in diminishing these risks becomes invaluable. The data suggests that
banks embracing fintech have a better track record of managing and mitigating non-performing
loans.

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Finally, the economic ramifications of this digital foray cannot be understated. The financial
implications of fintech innovations are profound. M. A. Chen et al. (2019) emphasize that emerging
technologies, notably IoT, robo-advising, and blockchain, revolutionize banks' operational
procedures. More importantly, they enhance the intrinsic value banks offer to their stakeholders,
from improved customer service to heightened security measures.
In conclusion, the digital transformation in banking is not a mere buzzword; it's a multi-faceted
revolution reshaping the very foundations of the financial sector. Its influences are broad, from
redefining operational norms and business models to refining risk assessment and management
processes. As the digital age progresses, the symbiosis between banking and technology is only
poised to deepen, with implications for institutions, consumers, and economies at large.
Digital transformation, a paradigm shifts reshaping industries worldwide, has garnered significant
attention within the banking sector. As banks grapple with integrating innovative technologies,
measuring the depth and breadth of this transformation becomes paramount. The varied
methodologies employed by researchers to capture this phenomenon are reflective of its
multifaceted nature and the evolving landscape of the banking domain.
Digital transformation, a contemporary buzzword, has become a focal point of academic research,
with various methodologies being employed to quantify its penetration and effect in the banking
sector. One prominent methodology leverages text analysis of annual reports. In the research
conducted by Nguyen et al. (2023), text analysis was employed on annual reports to discern the
levels of digital transformation in joint-stock commercial banks in Vietnam from 2015 to 2021.
This approach emphasizes the commitment and direction of banks towards digital initiatives as
outlined in their official communications to shareholders and the public. Other studies also extract
the information about digital transformation by natural language processing on public reports such
as annual reports, firm’s website news (Bai & Yu, 2021; Chen & Srinivasan, 2023; Hongbin et al.,
2021).
An innovative approach to gauge the extent of digital transformation within companies is through
textual analysis of their annual reports, specifically by counting the frequency of terms like "digital
transformation" and related phrases. This method hinges on the premise that increased mentions
reflect greater organizational emphasis on digital initiatives. Some recent studies have indeed
employed this technique, suggesting its growing acceptance as a legitimate measure of a
company's digital transformation endeavors (Qi & Cai, 2020; Wu et al., 2021; Yuan et al., 2021).
Another methodological avenue explored in the literature is the evaluation of hardware and
software adoption metrics (Liu et al., 2021; Z. Liu et al., 2020; Wang et al., 2017). For example,
Pierri and Timmer (2020) utilized a unique dataset to estimate the IT adoption intensity, capturing
the hardware components used across US commercial bank branches. This method provides a
tangible measure of technological uptake, giving insights into a bank's willingness and ability to
adapt to new digital trends.

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In a distinct approach centered around the burgeoning FinTech sector, Cheng and Qu (2020)
devised a FinTech index hinged on web crawler technology and word frequency analysis. Their
study showcased the progression of digital undertakings in Chinese banks, delineating the
acceleration of specific technological advances over others. This method offers a more granular
view of how banks are integrating emerging technologies into their operations. Further pushing
the boundaries of measuring digital transformation, M. A. Chen et al. (2019) utilized patent filings
data to identify and classify FinTech innovations. Using machine learning techniques, they were
able to sift through vast datasets, pinpointing the crux of technological advancements in the sector.
Besides, the case-based method stands as a pivotal approach in measuring digital transformation,
allowing for a detailed and contextual exploration of an organization's digital evolution. Unlike
quantitative strategies, the case-based approach delves deeply into specific instances of digital
transition, providing a rich narrative that captures both the nuances of implementation and the
challenges faced (Jiao et al., 2021). Through in-depth interviews, documentation reviews, and
observational techniques, researchers can piece together the intricate journey of digital adoption
and transformation within a firm (Qi et al., 2021). This method is particularly useful when studying
unique scenarios or groundbreaking initiatives, as it offers insights that broader surveys might
overlook. However, this method has constraints in terms of impartiality and broad applicability.
Some other research employed the survey method to measure the digital transformation (Dai et al.,
2020; Yang et al., 2021). By employing structured questionnaires, researchers can capture
perceptions, attitudes, and the extent of digital adoption across different departments and
hierarchical levels within firms. This method facilitates data collection from a larger sample,
providing a broader perspective on the subject matter. However, it is essential to note that while
the survey method offers quantitative insights, it may sometimes miss the intricate nuances and
complexities of digital transformation practices within an organization. Moreover, the validity of
the survey findings heavily depends on the design of the questionnaire and the accuracy of the
respondents' answers, leading to potential biases and misinterpretations.
The methodologies utilized to measure digital transformation, encompassing text analysis of
annual reports, hardware and software adoption metrics, patent filings evaluation, case-based
investigations, and structured surveys, each have their distinct strengths and limitations. Methods
such as textual analysis and patent filings evaluation are primarily quantitative and offer broad
insights yet may miss intricate specifics of actual implementation. In contrast, case-based studies
provide a deeper dive into individual instances of digital transition yet may grapple with challenges
of impartiality and wider applicability. Surveys, though expansive in reach, sometimes fall short
in capturing the complex nuances of digital transformation, being reliant on the respondent's
understanding and biases.
Given that digital transformation is a multifaceted concept, each method, with its unique vantage
point, captures only a segment of this vast domain. Recognizing the limitations inherent to each
method underscores the imperative for a comprehensive index, one that amalgamates multiple
dimensions of digital transformation. Such an index would provide a more holistic and accurate

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portrayal, bridging the gaps in our current understanding and measurement of digital
transformation in the banking sector.
Digital transformation, a multidimensional paradigm shift, transcends mere technological
implementations, significantly impacting the strategic, business, and managerial aspects of
organizations. Each of these dimensions offers a unique lens through which to comprehend and
navigate the complexities of digital transformation, ensuring that enterprises remain competitive
and agile in an increasingly digital era. Digital transformation can be broken down into three core
facets: strategic overhaul, operational evolution, and managerial reformation (Xie & Wang, 2023;
Yang et al., 2021).
 Strategy Transformation: Central to the process of digital adaptation, strategy
transformation pertains to the high-level planning and directional shifts an organization
undertakes in anticipation or response to the digital era (Bharadwaj et al., 2013). This often
involves reimagining business models, aligning the company's vision with digital
capabilities, and envisioning new value propositions that capitalize on digital technologies.
As Porter and Heppelmann (2014) elucidate, the strategic integration of digital technology
can lead to the creation of new products, services, and business models, thereby disrupting
existing industry structures and reshaping competitive dynamics.
 Business Transformation: This dimension involves the tangible changes to an
organization's core operations, encompassing areas like customer engagement, product and
service delivery, and overall operational efficiency. In the realm of digital transformation,
business transformation typically emphasizes optimizing customer experiences, leveraging
data analytics, and automating operations (Westerman et al., 2014). As businesses adopt
digital tools, they often see a shift from traditional operational models to more digitally
augmented or entirely digital models, thereby driving efficiency, scalability, and
innovation.
 Management Transformation: As organizations evolve digitally, there's an imperative to
redefine managerial and organizational structures, ensuring alignment with the new digital
strategy and business models. This encompasses the transformation of internal processes,
talent management, organizational culture, and decision-making frameworks (Kane et al.,
2015). In a digital age, the onus is on management to foster a culture of continuous learning,
innovation, and agility. Hierarchies may flatten, cross-functional collaboration can become
the norm, and decision-making might become more data-driven, all in a bid to support and
sustain digital initiatives.
In essence, these three dimensions of digital transformation, though distinct, are deeply
interconnected, each reinforcing and building upon the other. A truly successful digital
transformation initiative should holistically address these three facets, ensuring strategic
alignment, operational efficiency, and management adaptability in the face of rapid technological
advancements (Matt et al., 2015). Given the multifaceted nature of digital transformation,
organizations are encouraged to approach it as a comprehensive endeavor rather than isolated
digital initiatives.

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2.1.2 Bank’s performance
Bank performance is a multifaceted concept, underpinned by both financial and operational
metrics. It essentially provides a snapshot of how well a bank is operating in relation to its past, its
peers, or the industry as a whole. Financial indicators such as return on assets (ROA), return on
equity (ROE), and net interest margin (NIM) are commonly employed to gauge a bank's
profitability and efficiency (Rose & Hudgins, 2008). These metrics offer insights into the bank's
ability to generate returns on its investments and equity, thereby indicating its financial health and
sustainability.
Furthermore, the quality of a bank's assets, particularly its loan portfolio, is paramount. Non-
performing loans (NPL) serve as a crucial measure in this domain, reflecting the proportion of the
bank's loans that are at risk of default (Berger & DeYoung, 1997). A higher ratio of NPLs can
signify potential challenges in the bank's credit risk management, which could, in turn, imperil its
financial stability. Operational efficiency, another pivotal aspect of bank performance, entails the
bank's capability to manage its operations cost-effectively. Efficiency ratios, such as the cost-to-
income ratio, provide a lens to assess how adeptly a bank is converting its assets to revenue minus
its liabilities (Bourke, 1989). Customer service and satisfaction have also emerged as significant
non-financial metrics in evaluating bank performance in recent years. In an era where banking
services are becoming increasingly commoditized, the quality of customer interactions and
experiences can serve as differentiators and predictors of long-term profitability and sustainability
(Hitt et al., 1998).
In sum, bank performance transcends mere financial figures; it encapsulates a combination of
financial, operational, and qualitative measures, each offering a distinct perspective on the bank's
overall health and effectiveness. Given the research topic's focus on the ramifications of digital
transformation in banking, it is pertinent to gauge bank performance from both financial and
operational perspectives. Digital transformation inherently influences a bank's operational
efficiency and its subsequent financial outcomes (Humphrey & Pulley, 1997). As such, to
holistically comprehend the impact of digital interventions, it is crucial to evaluate banks through
the lens of their financial returns and operational processes. Scholars such as Berger and DeYoung
(2006) have accentuated that a singular focus on financial metrics might not sufficiently capture
the depth of changes digital transformation can instigate. Hence, in alignment with contemporary
academic discourse, considering both financial and operational performance dimensions in this
research is a fitting approach (Koetter & Noth, 2013).
2.2 Background Theories
2.2.1 Technology Acceptance Model
The Technology Acceptance Model (TAM), introduced by Davis (1989), stands as an integral
model delineating the determinants leading to the acceptance and consequent utilization of
information systems. At its core, TAM is anchored around two salient beliefs: "perceived ease of
use" and "perceived usefulness." The former reflects an individual's belief that using a specific
technology would be free from effort, whereas the latter encapsulates the conviction that using the
technology would enhance one's job performance (Davis, 1989).

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In the milieu of digital transformation in the banking sector, TAM's applicability becomes
pronounced. The banking arena is currently experiencing monumental shifts, underscored by
innovations such as AI-driven interactions, comprehensive online platforms, and cutting-edge
mobile banking applications. Given this landscape, understanding the dynamics influencing
technology adoption by both employees and clients becomes pivotal. Through the lens of TAM,
banks can glean insights into the key determinants swaying their employees towards or away from
new technological integrations, thereby informing training methodologies and refining internal
strategies (King & He, 2006).
Simultaneously, when viewed from the customers' perspective, TAM offers a valuable framework.
As banks explore and debut new digital platforms, understanding user perceptions about the ease
and utility of these platforms becomes a cornerstone for success. Leveraging TAM facilitates banks
in designing more user-oriented, intuitive systems, ensuring higher adoption rates (Alalwan et al.,
2017; Safeena et al., 2011). In summation, TAM's empirical foundation equips research on
banking's digital evolution with a robust mechanism to extract insights, driving the effective design
and rollout of advanced digital banking solutions.
2.2.2 Diffusion of Innovations Theory
The Diffusion of Innovations Theory, developed by Everett Rogers, is a seminal framework that
seeks to explain how new ideas, practices, and technologies spread and are adopted within social
systems. Central to this theory is the concept of an "innovation" – an idea, practice, or object
perceived as new by the adopting unit. According to Rogers, the diffusion process is shaped by
four key elements: innovation itself, communication channels, time, and the social system (Rogers
et al., 2014). Rogers categorized adopters into five groups based on their adoption speed and
characteristics: innovators, early adopters, early majority, late majority, and laggards. Each
category represents a segment of adopters who approach innovations differently. Factors like
perceived benefits, risks, compatibility with existing values and practices influence complexity of
the innovation influence the rate of adoption.
Within the landscape of banking and its digital transformation, the Diffusion of Innovations Theory
offers a compelling lens to interpret the varying rates at which banks adopt and integrate novel
digital technologies into their operations. For instance, while some banks (innovators) might be
swift to experiment with and embrace emerging fintech solutions, others (laggards) may be more
circumspect, waiting for more widespread industry validation before integrating these
technologies. This theory can be particularly insightful for strategists and decision-makers in the
banking sector, allowing them to understand the barriers and facilitators affecting the adoption of
digital technologies. Recognizing where their institution stands on the adoption curve can also
inform tailored strategies to promote quicker uptake or to manage potential risks associated with
being an early or late adopter. Empirical studies have leveraged the Diffusion of Innovations
framework to understand technology adoption in the financial sector (Chen & Srinivasan, 2023;
Thakur & Srivastava, 2014; Wu et al., 2021).

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2.2.3 Resource-Based View (RBV)
The Resource-Based View (RBV) of the firm, rooted in the field of strategic management,
proposes that a company can achieve a sustainable competitive advantage through the application
and orchestration of its unique, valuable, rare, and inimitable resources (Barney, 1991). This
framework positions internal organizational resources as key determinants of firm performance.
Instead of solely focusing on external competitive factors, the RBV emphasizes internal
capabilities, skills, and assets, suggesting that firms can create a sustainable advantage when they
exploit these inimitable resources in environments where competitors cannot easily replicate them.
Within the realm of banking's digital evolution, the RBV offers crucial insights. Financial
institutions are equipped with an array of assets, encompassing both tangible elements (such as IT
systems and physical outlets) and intangible facets (like brand equity, technological expertise, and
the trust of their clientele). The RBV posits that in the context of digital transformation, it's not
merely about embracing novel technologies. Banks should strategically deploy their distinctive
assets in ways that set them apart from their rivals.
Consider this: multiple banks might integrate AI-enhanced customer interactions. However, a bank
that has amassed extensive historical client data (an invaluable asset) can optimize its AI models
more effectively, resulting in enhanced client service experiences. Such strategic utilization of
inherent resources during the digital overhaul can cultivate enduring competitive edges. Numerous
research endeavors within the finance sector have employed the RBV lens to delve into the
influence of institutional assets on performance metrics (Bharadwaj, 2000; Nambisan et al., 2017;
Xie & Wang, 2023).
2.2.4 Dynamic Capabilities Theory
The Dynamic Capabilities Theory, as introduced by Teece et al. (1997), underscores the
significance of an organization's capacity to integrate, build upon, and reconfigure both internal
and external competencies in response to the dynamic and rapidly changing environment. This
approach to strategic management highlights the importance of adaptability, flexibility, and the
transformative potential of firms, suggesting that static capabilities are insufficient in constantly
evolving markets.
Within the context of the banking sector's digital transformation, this theory holds particular
resonance. The digital era, characterized by rapid technological advancements and ever-evolving
consumer preferences, necessitates that banks not only adopt but continually adapt to remain
competitive. Here, the Dynamic Capabilities Theory can serve as a lens to study how banks
reconfigure their existing assets and capabilities, embrace novel technologies, and forge new
alliances, ensuring that they remain at the forefront of the digital banking evolution.
Empirical studies further enrich our understanding of the application of this theory in the financial
sector. For example, a study by Wilden et al. (2016) applied the Dynamic Capabilities framework
to explore how firms, including those in the financial sector, can achieve competitive advantage
through the alignment of their dynamic capabilities with the technological environment. Their
findings emphasized the need for firms to develop an ambidextrous approach, balancing

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explorative and exploitative strategies to navigate the challenges and opportunities of digital
transformation. Moreover, Zott and Amit (2010) highlighted the role of dynamic capabilities in
shaping a firm's transactional structures, particularly emphasizing how digital innovations
necessitate changes in both external customer-facing activities and internal organizational
processes. Their insights can be particularly useful for banks, as they reimagine their operational
models in the face of digital disruptions.
In conclusion, the Dynamic Capabilities Theory provides an invaluable framework for researchers
to explore the strategic maneuvers banks undertake, emphasizing adaptability and continuous
evolution in the face of the digital age's challenges and opportunities.
2.2.5 Technology Acceptance Model
The Institutional Theory, as expounded upon by Scott (2014) and others, posits that organizational
behavior is profoundly influenced by institutional pressures from the broader environment. These
pressures can be broadly categorized into three pillars: regulative (laws, regulations), normative
(social norms, values), and cognitive (shared beliefs, conceptions). Organizations, in order to gain
legitimacy and enhance survival prospects, often conform to these pressures, leading to processes
of isomorphism where organizations in the same field become increasingly similar over time.
In the context of digital transformation in banking, the Institutional Theory offers a robust
framework to understand the underlying factors propelling or constraining banks' digital
adaptation. Regulatory pressures, for instance, can manifest in directives related to digital
payments, data protection, or cybersecurity, thereby influencing the trajectory of digital strategies
in banks. Normative pressures, stemming from industry best practices or evolving customer
expectations, can drive banks to adopt particular digital platforms or technologies to remain
competitive and relevant. Cognitive pressures, emerging from collective beliefs about the role of
technology in banking, can shape the bank’s overarching digital vision and its alignment with
stakeholders' expectations. Empirical studies employing the Institutional Theory in financial
contexts further elucidate its application (Kumar, 2014; Mohamed & Salah, 2016; Pramanik et al.,
2019; Yuliansyah et al., 2016).
In summary, the Institutional Theory provides a multifaceted lens for researchers to delve into the
myriad external pressures shaping the digital transformation trajectories of banks. Recognizing
and deciphering these institutional forces can be pivotal in understanding the heterogeneity in
digital strategies and practices across the banking sector.
2.3 Empirical Studies
The wave of digitalization sweeping across various industries has notably reshaped the banking
sector. While the promise of improved efficiency and customer satisfaction looms large, academic
scrutiny reveals a multi-faceted impact of digital transformation on bank performance.
Most of the papers on this subject address the impact of digital transformation on the
competitiveness and financial performance of banks, especially in the face of global challenges
like the COVID-19 pandemic. For instance, Kolodiziev et al. (2021) explored the competitiveness

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of Ukrainian banks in the face of the rapid spread of electronic payments, e-commerce, and digital
services. Similarly, N. T. H. Nguyen et al. (2022) undertook an exploration into the effects of
digital banking on the financial performance of Vietnamese banks during the pandemic, revealing
the critical role of customer experience in this dynamic.
Various methodologies have been used in these studies. The standard methods include statistical
analysis, correlation, and regression analysis. Kolodiziev et al. (2021), for instance, utilized
standardized input statistical indicators, cluster analysis, and regression and correlation analysis to
assess the impact of digitalization on Ukrainian banks. Van Thuy (2021), in his empirical
examination of the link between ICT and bank performance in Vietnam, employed a data-driven
approach, using financial indicators from 20 Vietnamese banks over a 12-year period. The hybrid
MCDM method, a fusion of CRITIC, DEMATEL, and TOPSIS, was also utilized by P.-H. Nguyen
et al. (2022) to evaluate Vietnamese banks' performance under the impact of COVID-19.
The findings in these studies overwhelmingly indicate a positive correlation between digital
transformation and bank performance. For instance, in the Ukrainian context, banks experienced
increased competitiveness through the adoption of digital banking technologies (Kolodiziev et al.,
2021). In the Vietnamese context, the adoption of new information and communication
technologies led to notable transformations, significantly impacting bank performance (Van Thuy,
2021). Furthermore, during the COVID-19 pandemic, digital banking played a pivotal role in
determining financial outcomes, with customer experience being a significant determinant (N. T.
H. Nguyen et al., 2022). However, there's also an emphasis on the need for intelligent risk
management systems and swift digital transformation in such contexts (P.-H. Nguyen et al., 2022).
Besides, while many researchers argue that digital transformation can boost a company's
performance by mitigating information gaps and fostering R&D advancements (Wu et al., 2021),
others suggest that the intricacies of management and the significant initial investments might
negate these benefits. For instance, Qi and Cai (2020) investigated publicly traded manufacturing
firms in China and discovered that while digital transformation can impact company performance
through management and sales operations, the effects from these two areas may neutralize each
other, leading to an overall negligible effect of digital transformation on company performance. In
a similar vein, Hajli et al. (2015) reported that not all firms experience a direct positive relationship
between digital technology and their performance; only select companies can truly harness the
benefits of digital transformation. These findings underscore the multifaceted nature of digital
transformation. Although it has the potential to bolster company capabilities and trim expenses,
the returns from such transformations can be unpredictable and hinge on how they are integrated
and executed.
In conclusion, the digital transformation in the banking sector remains a vital area of research, with
numerous studies illustrating its impact on bank performance. However, the complexities of global
challenges, such as pandemics, emphasize the need for ongoing research and adaptation within the
sector. Future research might benefit from more global studies, the incorporation of emerging
technologies, and an examination of evolving customer expectations in the digital age.

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3. Methodology
3.1 Variables measurement
3.1.1 Proxy for bank’s digital transformation
Digital transformation encapsulates the deliberate integration of digital technologies into a variety
of organizational elements, including products, processes, and structural foundations (Feyen et al.,
2021). To remain competitive in a rapidly digitizing world, many enterprises have embraced this
transformative journey, particularly commercial banks (Y. Liu et al., 2020; Yoo et al., 2010). The
intricate process of digital transformation can be delineated into three distinct dimensions: strategy
transformation, business transformation, and management transformation (Liu, 2020; Xie & Wang,
2023; Yang et al., 2021). Each dimension serves a specific role; while the strategic component lays
the foundation, the subsequent business and management shifts are reflective of this strategy.
Furthermore, it's noteworthy how these dimensions are intertwined, with management
transformation spurring business adaptability and amplifying strategic directives. Consequently,
this paper follows the method of conducting an index system to measure the digital transformation
of Xie and Wang (2023), scrutinizes the digital evolution of commercial banks through the lens of
these three pivotal dimensions.
First, strategy transformation in the banking sector denotes the emphasis banks place on digital
technology at a strategic level. This is commonly gauged by examining the recurrence of digital
technology-related terms within the annual reports of these financial institutions (Xie & Wang,
2023). While the method of keyword frequency has been a preferred choice among many
researchers to quantify digital transformation, it's worth noting that the selection of these keywords
can often be influenced by subjective interpretations (Qi & Cai, 2020; Wu et al., 2021). Moreover,
there's a risk that certain terms linked to nascent technologies might be overlooked, potentially
skewing the results and underscoring the need for a more comprehensive and objective approach
(Xie & Wang, 2023).
To measure the strategy transformation, this study adopts the text learning methodology proposed
by Hassan et al. (2019), leading us to establish a "digital technology-centric text library." This
dedicated text library encompasses a comprehensive collection of documents specifically related
to digital technology. By tallying the count of pre-defined keywords within annual reports, we can
gauge the emphasis on digital technology-related terms. A higher count of such keywords signifies
that banks are prioritizing digital technology, which in turn points to a more evolved phase of
strategy transformation.
Second, business transformation zeroes in on the extent to which banks incorporate digital
technology into their range of financial services. The infusion of digital technology not only
broadens the interaction pathways between banks and their clients but also allows these financial
institutions to cater to niche demands, facilitating tailored product innovations. As a consequence,
there's a shift in the bank's R&D innovation trajectory. To comprehensively gauge the business
metamorphosis induced by digital technology, this study follows the method of Xie and Wang
(2023), appraise it from three distinct perspectives: (i) digital channels - the expansion into digital

13
channels, (ii) digital products - the development of digital-centric products, and (iii) digital R&D
- the evolution of research and development in the digital domain.
To delve into the specifics, the aspect of digital channels is quantified in this study by counting the
variety of channels that banks employ to deliver their products or services using digital mediums
to their customers. Subsequently, the dimension of digital products is assessed by enumerating the
total amount of unique digital products or services provided by the banks. The third facet, digital
R&D, is gauged by scanning the abstracts of banks' patent applications.
Third, the aspect of management transformation emphasizes the extent to which banks integrate
digital technology into their organizational structure and governance procedures. With regards to
internal management processes, digital innovations can reshape the traditional workflows of an
institution, prompting significant shifts in governance approaches and organizational layouts (Liu
et al., 2021; Qi & Cai, 2020). Hence, to gauge management transformation in banks, this study
also follows the method of Xie and Wang (2023), assesses it across three specific areas: (i) digital
structure - the adoption of digital infrastructures, (ii) digital structure - the cultivation of digital
expertise, and (iii) digital collaborations - the fostering of digital partnerships.
For digital structure, this study assesses this criterion by two significant shifts within banks'
organizational structure. Firstly, there's an internal restructuring observed, marked by the creation
of departments such as Internet finance, digital finance, or fintech. Secondly, banks are establishing
fintech subsidiaries, which operate outside the bank's traditional organizational structure,
facilitating a focused approach to digital innovation. For the dimension of digital talents, our metric
is the ratio of senior executives and board members possessing an IT background within the bank's
leadership. This IT background is gauged both by their educational credentials and their
professional history. Educationally, we identified if the individual pursued studies in fields like
computer science, software engineering, or information science. Professionally, we checked if the
individual had experience working at an IT firm or held roles such as a bank's chief information
officer. Lastly, in evaluating digital collaboration, we employed text analysis on annual reports,
specifically seeking out terms like "partnership" and "collaboration", to ascertain if the bank had
forged any alliances with external tech entities.
Table 1: Measurement of Digital Transformation
Main Indicators Sub Indicators Measurement
Strategy Transformation None Number of words in “digital technology-
centric text library” appear in the annual report
Business Transformation Digital Channel Number of digital channels
Digital Products Number of digital products
Digital R&D Number of patents
Management Transformation Digital Architecture Number of Internet finance, digital finance, or
fintech department
Digital Talents Proportion of members with IT background or
profession in the board of directors

14
Digital Collaboration Number of words "partnership" and
"collaboration" appear in the annual report
Source: Author
The table above summarizes the measurement of the indicators for digital transformation.
However, before incorporating the indicators within each dimension, it's essential to establish their
respective weights. When constructing indexes, scholars have traditionally employed the principal
component analysis (PCA) method to establish the weight of indicators, as seen in studies by
various researchers such as (Ang & Bekaert, 2007; Bekaert et al., 2003; Billio et al., 2010; R. Chen
et al., 2019). What distinguishes the PCA method is its inherent objectivity. Essentially, this
technique allows the weight to be dictated by the data's intrinsic characteristics. In other words,
factors exhibiting larger variations carry more significant weight. This approach is impervious to
any external, subjective influences.
Given that digital transformation represents a relatively novel concept in the academic sphere, it's
crucial to minimize biases and potential distortions introduced by subjective judgment. In this
context, PCA emerges as an especially apt method for determining indicator weight. By relying on
data-driven characteristics, the PCA method ensures that the weightings derived are robust,
authentic, and reflective of the actual importance of the indicators in the digital transformation
landscape.
In assessing the suitability of our dataset for the application of principal component analysis
(PCA), two pivotal tests were conducted: the Kaiser-Meyer-Olkin (KMO) measure of sampling
adequacy and Bartlett's test of sphericity. The KMO measure is a statistic that indicates the
proportion of variance in the variables (indicators) that might be caused by underlying factors.
PCA (Principal Component Analysis) and Factor Analysis operate under the assumption that there
are underlying patterns in the data that can be summarized using fewer new variables (components
or factors) (Costello & Osborne, 2005). If there's no such underlying pattern, these methods aren't
useful. The KMO test measures sampling adequacy for each variable in the model and for the
complete model. The KMO returns values between 0 and 1 (Kaiser, 1974). In that:
 A value of 0 indicates that the sum of partial correlations is large relative to the sum of
correlations, indicating factor analysis is likely inappropriate.
 A KMO value close to 1 suggests that patterns of correlations are relatively compact and
so factor analysis should yield distinct and reliable factors.
Besides, Bartlett’s Test of Sphericity checks whether or not the observed variables intercorrelate
at all using the observed correlation matrix against the identity matrix (Bartlett, 1954). If the test
found that the observed correlation matrix is an identity matrix, it would not be suitable for factor
analysis. A significant p-value for Bartlett’s test indicates that your observed correlation matrix is
not an identity matrix and hence is suitable for factor analysis (Dziuban & Shirkey, 1974; Hair et
al., 2010).
In summary, KMO value exceeding 0.6 is commonly considered as an acceptable threshold,
suggesting that a significant proportion of the variance has been captured by the underlying factors
and justifying the use of factor analysis methods like PCA (Kaiser, 1974). Furthermore, Bartlett's

15
test of sphericity ascertains the hypothesis that the original correlation matrix is an identity matrix,
indicating that the dataset is not factorizable. A significant p-value (less than 0.05) for Bartlett's
test indicates that a factor analysis may be useful with the data (Bartlett, 1954). Given that if
computed KMO value surpassed the 0.6 threshold, combined with a significant outcome from
Bartlett's test, then the index system is appropriately tailored for the PCA approach.
Upon determining the weights for each sub-indicator, the values of the primary indicators were
ascertained by multiplying them with their respective sub-indicator weights. Following this
process, the Digital Transformation Index (DTI) was derived by multiplying the values of these
primary indicators, as computed earlier, with their associated weights. This methodological
approach ensures that each facet of the index is appropriately weighted, reflecting its importance
within the broader framework of digital transformation in the context of our research. However, to
guarantee the objectivity of data values and mitigate the potential impacts of varying scales within
the dataset, the author employed the MinMaxScaler approach to normalize the data prior to
multiplying the individual indicator values with their respective weights. Using this method
ensures consistency and comparability across the dataset, rendering the results more reliable and
interpretable (Jolliffe & Cadima, 2016; Zaki & Meira, 2014).
3.1.2 Proxies for bank’s performance
The digital transformation of banks and its impacts on the financial services industry has become
a prominent topic in recent academic literature. Digital transformation can be characterized by
significant improvements in the connectivity of systems, computing power and cost, and the
creation and usability of data. The influence of digital innovation on the financial industry has
been substantial, enabling the unbundling and disaggregation of financial services. The ease of
information exchange and reduced transaction costs have allowed specialized players to offer
individual services, letting consumers assemble their preferred product suite (Feyen et al., 2021).
Moreover, digital technology has led to new business models and the emergence of new entrants,
intensifying competition, and reshaping market structures. The benefits of digital transformation
are not uniformly distributed across all activities. Beccalli (2007) demonstrated that while
investment in IT services from external providers could have a positive influence on banks' profits
and efficiency, the acquisition of hardware and software could potentially reduce their
performance. This finding underscores the need for strategic planning in the implementation of
digital transformation initiatives.
So, digital transformation, as a sweeping paradigm shift in the banking sector, is not merely a
technological upgrade but a comprehensive restructuring of the way banks operates, offer services,
and interact with their stakeholders. Consequently, its impacts on bank performance are
multifaceted and profound, touching on operating, financial, and risk metrics. In this study, the
authors use three different proxies to measure the bank’s performance: namely CIR for operating
performance, and NIM for financial performance.
Operating Performance: One of the central arguments in favor of digital transformation is the
potential efficiency gains. As banks integrate advanced digital systems, one expects operational
processes to become streamlined, leading to reduced operational costs. Cost-to-Income Ratio

16
(CIR), which provides a clear lens into the bank's operational efficiency by comparing its costs to
its income, is a pivotal metric here. A more digitized bank would ideally witness a consistent
decline in its CIR, as its operations become more efficient and less reliant on manual and resource-
intensive processes (Beccalli, 2007).
CIR = Operating Costs / Operating Income
The CIR indicates the operational efficiency of a bank. A lower CIR indicates higher efficiency,
suggesting that the bank is incurring fewer costs to generate each unit of income. A higher CIR
might indicate that the bank is spending more to generate each unit of income, which is not optimal
for profitability (Molyneux et al., 2006).
Financial Performance: Digital transformation has the potential to revolutionize banks' revenue
models. With the enhanced ability to tailor financial products to specific consumer segments and
rapidly adapt to market changes, banks can optimize their Net Interest Margin (NIM). This
optimization would mean banks can better manage the spread between interest income generated
from their assets and the interest paid out on their liabilities. (Feyen et al., 2021) articulate how
technological advancements allow for improved connectivity of systems and data usability, which
can be leveraged to enhance financial performance metrics such as NIM.
NIM = (Interest Income – Interest Expense) / Average Earning Assets
NIM measures the difference between the interest income generated by banks and the amount of
interest paid out to their lenders, relative to the average amount of their interest-earning assets. It
indicates how successfully the bank's investment decisions are compared to its debt situations
(Rose & Hudgins, 2008).
3.1.3 Control Variables
When examining complex relationships, like the impact of digital transformation on bank
performance, it's essential to account for external and internal factors that could influence the
outcome. Banks vary in their size, organizational structure, market reach, and a plethora of other
factors. For instance, a large multinational bank may have a different baseline performance
compared to a regional community bank, irrespective of their digital transformation strategies. By
incorporating control variables such as bank size, age, or capital structure, we ensure that the
observed effects on performance are indeed attributable to digital transformation and not
confounded by these other inherent bank characteristics. Following the model of Xie and Wang
(2023), the control variables used in this study include: (i) bank size, (ii) equity to asset, (iii) loan
to deposit, (iv) loan to asset, and (v) board independence.
Bank size, usually proxied by the natural logarithm of total assets, is one of the most frequently
incorporated control variables in banking studies. Larger banks often have economies of scale,
diversified portfolios, and different risk management strategies compared to their smaller
counterparts (Berger & DeYoung, 2006). Thus, controlling for bank size ensures that the observed
effects on performance aren't merely a reflection of the inherent advantages or disadvantages
associated with a bank's size.

17
The equity to asset ratio is an indicator of a bank's financial leverage and its ability to absorb
potential losses. It's pivotal in assessing a bank's risk profile. Research by Goddard et al. (2004)
emphasized its role in understanding bank profitability and ensuring that the outcomes observed
aren't influenced by the underlying risk levels of individual banks. The loan to deposit ratio is a
classic metric indicating a bank's liquidity position. Banks with a high loan to deposit ratio might
be seen as taking more risk by lending extensively. Demirgüç-Kunt and Huizinga (1999) found
this ratio to be relevant when analyzing bank interest margins and profitability, suggesting its
appropriateness as a control variable.
The loan to asset ratio offers insights into the bank's asset allocation. A higher ratio could signify
aggressive lending practices. Dietrich and Wanzenried (2014) highlighted its importance when
investigating the determinants of bank profitability, underscoring its relevance as a control. Board
independence, often gauged by the ratio of independent directors to total directors, captures the
bank's governance quality. An independent board can mitigate agency problems and influence risk-
taking behavior (Erkens et al., 2012). Including this control ensures that the observed bank
performance isn't just a function of its governance structure.
Table 2: Variables Description
Variables Proxy Formula References
Bank’s
Performance
Net Interest Margin
(NIM)
(Interest Income – Interest
Expense) / Average Earning
Assets
Feyen et al. (2021)
CIR Operating Costs / Operating
Income
Beccalli (2007)
Digital
Transformation
Digital
Transformation
Index
Weighted average of 3 main
criteria
Qi and Cai (2020); Xie
and Wang (2023)
Strategy
Transformation (ST)
Scaled number of words in
“digital technology-centric text
library” appear in the annual
report
Hassan et al. (2019);
Wu et al. (2021); Xie
and Wang (2023)
Business
Transformation (BT)
Weighted average of digital
channel, digital product, and
digital R&D
Liu (2020); Xie and
Wang (2023)
Management
Transformation
(MT)
Weighted average of digital
architecture, digital talent, and
digital collaboration
Qi and Cai (2020); Xie
and Wang (2023)
Control
Variables
Bank Size (SIZE) Natural logarithm of Total Asset Berger and DeYoung
(2006)
Equity to Asset (EA) Equity to Asset ratio Goddard et al. (2004)
Loan to Deposit
(LD)
Loan to Deposit ratio Demirgüç-Kunt and
Huizinga (1999)

18
Loan to Asset (LA) Loan to Asset ratio Dietrich and
Wanzenried (2014)
Board Independence
(IND)
Number of independent member
in the board / Total number of
members in the board
(Erkens et al., 2012)
Source: Author
3.2 Data processing procedure
In this study, authors sourced data from commercial banks listed on the Vietnam Stock Exchange,
spanning a period from 2017 to 2022. The financial data was primarily extracted from the Refinitiv
database, a reputable source of financial market data. In addition to the financial metrics, data
related to digital transformation was manually collated, adhering to the index metrics introduced
by the author, which provided a more tailored insight into the specific area of interest.
The data processing encompassed several stages to ensure the integrity and accuracy of the dataset.
Initially, data was gathered from both Refinitiv and through manual collection methods.
Subsequent to the initial collection phase, the dataset was meticulously cleaned, eliminating
outliers that could skew the results and removing any entries with missing or incomplete
information. After this refinement process, the final dataset was comprised of 27 distinct
commercial banks. This culminated in a total of 162 observations, providing a robust dataset for
analysis and ensuring the findings drawn from this study are grounded in comprehensive empirical
evidence.
The data processing procedure includes the following steps: (i) Data Collection and Preparation,
(ii) Pre-processing and Data Cleaning, (iii) Weight Calculation and Indicator Valuation, (iv) Model
Formulation and Regression Analysis, and (v) Conclusion Formulation. The details of each steps
are as follows:
 Data Collection and Preparation: The data pertinent to this study was obtained from dual
sources. Primarily, the financial information was garnered from the Refinitiv database, a
reputable secondary data repository known for its exhaustive collection of financial
metrics. Complementing this, data regarding digital transformation, delineated by the
criteria established earlier in this paper, were meticulously hand-collected from the annual
reports of the respective banks.
 Pre-processing and Data Cleaning: Upon collation, the data underwent a rigorous pre-
processing phase. This pivotal stage ensured the removal of empty records and outliers
which could skew results or introduce bias. This step was fundamental in preserving the
integrity of the subsequent analyses, ensuring that only robust and representative data was
considered.
 Weight Calculation and Indicator Valuation: Subsequent to the pre-processing, raw data
associated with digital transformation underwent a process of weight calculation. The
weights of each sub-indicator, as well as the primary indicators, were ascertained utilizing
the Kaiser-Meyer-Olkin (KMO) measure and the Bartlett's Test criteria. This involved the

19
employment of the Principal Component Analysis (PCA) methodology to delineate the
weights in a manner most reflective of the data's intrinsic structure. Following the
establishment of these weights, the valuation of sub-indicators and primary indicators for
the digital transformation variable was computed.
 Model Formulation and Regression Analysis: With the data effectively structured and
weighted, the focus shifted towards the development of the regression model. The model
sought to elucidate the relationship between the key variables. Notably, the performance
variable was operationalized through three proxies: Net Interest Margin (NIM), Cost-
Income Ratio (CIR), and Non-Performing Loans (NPL). Concurrently, the digital
transformation variable was represented through the aggregate digital transformation
index, supplemented by indices for the three main indicators: strategy, business, and
management. This led to a comprehensive exploration through twelve distinct models, each
offering nuanced insights into the interplay between digital transformation and bank
performance.
 Conclusion Formulation: Post regression analysis, the findings were synthesized to draw
meaningful conclusions. These inferences were framed in the context of the research
objectives, shedding light on the intricate dynamics between digital transformation
initiatives and their tangible effects on bank performance metrics.
3.2 Model
This investigation is guided by the research model proposed by Xie and Wang (2023), as detailed
below.
Performancei, t = β0 + β1DTIi, -1 + β2SIZEi, t-1 + β3EAi, t-1 + β4LDi, t-1 + β5LAi, t-1 + β6INDi, t-1 + ε
In that:
- Performancei, t: The performance of the bank i at year t, measured by NIM, and CIR.
- DTIi, t: The digital transformation index of bank i at year t-1, measured by overall index,
strategy transformation index (ST), business transformation index (BT) and management
transformation index (MT).
- SIZEi, t: The size of bank i at year t-1.
- EAi, t: The equity to asset ratio of bank i at year t-1.
- LDi, t: The loan to deposit ratio of bank i at year t-1.
- LAi, t: The loan to asset ratio of bank i at year t-1.
- INDi, t: The board independence ratio of bank i at year t-1.
- β0, β1, β2, β3, β4, β5, β6: are coefficients.
- ε: error term.
In this research, the authors have employed the linear regression technique. Linear regression is
especially apt for this study as it allows us to understand and quantify the relationship between
digital transformation and bank performance. Given the continuous nature of our dependent
variable, which in this case is bank performance, linear regression can effectively capture the
direction and strength of the relationship between our predictor variables, notably digital

20
transformation metrics, and the outcome. Moreover, this statistical method is renowned for its
capacity to provide a clear view of the potential predictors' impact on the outcome, while
controlling for other variables. As such, this study seeks to understand the nuanced influence of
digital transformation on bank performance amidst other factors, the linear regression technique
proves to be a valuable tool.
4 Results & Discussion
4.2 Descriptive Analysis
The table presented below enumerates the banks from which the data for this research was
gathered.
Table 3: List of banks in the research
# Symbol Name Founded Public Exchange
1 ABB An Binh Commercial Joint Stock Bank 1993 2020 UPCOM
2 ACB Asia Commercial Joint Stock Bank 1993 2020 HOSE
3 BAB BAC A Commercial Joint Stock Bank 1994 2021 HNX
4 BID Joint Stock Commercial Bank for Investment and
Development of Vietnam
1957 2014 HOSE
5 BVB Viet Capital Commercial Joint Stock Bank 1992 2020 UPCOM
6 CTG Vietnam Joint Stock Commercial Bank of Industry
and Trade
1988 2009 HOSE
7 EIB Vietnam Export Import Commercial Joint Stock 1989 2009 HOSE
8 HDB Ho Chi Minh city Development Joint Stock
Commercial Bank
1990 2018 HOSE
9 KLB Kien Long Commercial Joint Stock Bank 1995 2017 UPCOM
10 LPB LienViet Commercial Joint Stock Bank – Lienviet
Post Bank
2008 2020 HOSE
11 MBB Military Commercial Joint Stock Bank 1994 2011 HOSE
12 MSB The Maritime Commercial Joint Stock Bank 1991 2020 HOSE
13 NAB Nam A Commercial Joint Stock Bank 1992 2020 UPCOM
14 NVB National Citizen bank 1995 2010 HNX
15 OCB Orient Commercial Joint Stock Bank 1996 2021 HOSE
16 PGB Petrolimex Group Commercial Joint Stock Bank 1993 2020 UPCOM
17 SGB Saigon Bank for Industry & Trade 1987 2020 UPCOM
18 SHB Saigon-Hanoi Commercial Joint Stock Bank 1993 2021 HOSE
19 SSB Southeast Asia Commercial Joint Stock Bank 1994 2021 HOSE
20 STB Saigon Thuong Tin Commercial Joint Stock Bank 1991 2006 HOSE

21
21 TCB Viet Nam Technological and Commercial Joint
Stock Bank
1993 2018 HOSE
22 TPB TienPhong Commercial Joint Stock Bank 2008 2018 HOSE
23 VAB Viet A Commercial Joint Stock Bank 2003 2021 UPCOM
24 VBB Viet Nam Thuong Tin Commercial Joint Stock
Bank
2007 2019 UPCOM
25 VCB Joint Stock Commercial Bank for Foreign Trade of
Vietnam
1963 2009 HOSE
26 VIB Vietnam International Commercial Joint Stock
Bank
1996 2020 HOSE
27 VPB Vietnam Commercial Joint Stock Bank for Private
Enterprise
1993 2017 HOSE
Source: Author
The cumulative measure of sampling adequacy (KMO) for the indicators within the digital
transformation matrix stands at 0.667, surpassing the threshold of 0.6. This suggests that the data
is well-suited for factor analysis. Further, the Bartlett's test yields a p-value of 3.27e^-17, which is
considerably below the conventional 0.05 significance level. This result emphasizes that the
dataset's variables are interconnected sufficiently for principal component analysis (PCA). The
combined results of the KMO and Bartlett's test underscore the appropriateness of employing PCA
on this index system. The table below describes the result of PCA to define weights for each sub-
indicator and main indicator.
Table 4: Weights calculated by PCA
Main indicators Weights Sub-Indicator Weights
Strategy Transformation 17.9% Number of words in “digital technology-
centric text library” appear in the annual
report
100%
Business Transformation 41.3% Digital Channel 36.2%
Digital Products 30.6%
Digital R&D 33.2%
Management Transformation 40.8% Digital Architecture 44.0%
Digital Talents 43.1%
Digital Collaboration 12.9%
Source: Author
In the evaluation of the digital transformation index, three primary indicators are identified:
strategy transformation, business transformation, and management transformation. Their
respective weights underline their significance; strategy transformation holds a foundational yet
lighter role with a weight of 17.9%. In contrast, business transformation and management

22
transformation are more influential, with the former marginally leading at 41.3% compared to the
latter's 40.8%.
Strategy transformation is unique in its standalone significance, devoid of any sub-categories,
emphasizing its comprehensive and foundational role in the digital transformation journey.
Business transformation, on the other hand, consists of nuanced facets represented by its sub-
indicators. Among these, the digital channel stands out as the most influential, followed closely by
digital R&D, while the digital product accounts for the remainder.
Similarly, management transformation unveils a tri-dimensional structure. Digital architecture and
digital talents predominantly shape this category, both holding near-parallel significance.
Conversely, digital collaboration, although holding a smaller weight, completes the management
transformation narrative, signifying its integral, albeit smaller, role.
By articulating the digital transformation process in this stratified manner, the emphasis is not just
on the individual components but also on the intricate interplay of weights, showcasing the relative
importance of each dimension within the overarching digital transformation landscape.
Furthermore, the descriptive summary of indicators of the digital transformation index are
described by the table below:
Table 5: Descriptive statistics for indicators of the digital transformation index
Frequency Digital
channel
Digital
products
Digital
R&D
Digital
architecture
Digital
talents
Digital
cooperation
count 162 162 162 162 162 162 162
mean 48.40 3.90 1.43 14.48 1.30 0.10 28.24
std 50.19 1.21 1.59 37.12 0.65 0.08 25.83
min 0 0 0 0 0 0 2
25% 18 3 0 1 1 0.007 11
50% 33.5 4 1 4 1 0.09 21
75% 49.75 5 2 7.75 2 0.14 35.75
max 277 6 12 227 3 0.44 148
Source: Author
The data underscores the heterogeneous digital transformation landscape among banks. While
some are heavily integrating digital technology, as evident from frequent mentions in their annual
reports, others seem to be at the nascent stages of this transformation. It's notable that the average
number of digital channels and products are relatively low, suggesting room for growth or that
many banks may still be testing the waters.
The considerable standard deviation in the Digital R&D category points to a disparity in
technological investments, with certain banks being innovation powerhouses and others lagging.
The Digital Talents metric paints a telling picture of board compositions. With only a 10% average
representation from IT backgrounds, it raises questions about whether banks are adequately

23
leveraging tech expertise at the highest decision-making levels. Lastly, the high average mention
of collaboration terms suggests that partnerships, possibly with fintech firms or technology
providers, are a prominent theme in the digital strategies of many banks. This could be an avenue
to quickly harness innovative solutions without building them in-house.
The subsequent table provides a descriptive summary of all the variables included in the regression
model, offering insights into their distribution, central tendencies, and variability:
Table 6: Descriptive statistics variables in regression model
NIM CIR NPL DTI SI BI MI SIZE EA LD LA IND
count 162 162 162 162 162 162 162 162 162 162 162 162
mean 0.03 0.48 0.03 0.21 0.18 0.4 0.17 343,351 0.08 0.94 0.62 0.17
std 0.01 0.14 0.03 0.11 0.18 0.14 0.08 422,579 0.03 0.14 0.1 0.07
min 0.007 0.22 0.001 0.066 0 0.006 0.02 20,374 0.03 0.55 0.001 0
25% 0.02 0.36 0.01 0.14 0.01 0.29 0.1 89,885 0.06 0.85 0.57 0.13
50% 0.03 0.48 0.02 0.18 0.52 0.39 0.14 175,896 0.07 0.93 0.63 0.17
75% 0.04 0.57 0.03 0.25 0.78 0.49 0.21 383,653 0.1 1.01 0.68 0.2
max 0.09 0.87 0.24 0.6 1 0.81 0.4 2,120,609 0.17 1.43 0.79 0.5
Note: Unit of SIZE is billion VND
Source: Author
Upon analyzing the dataset detailing financial and digital transformation metrics, several
intriguing insights become apparent. For instance, while the Net Interest Margin (NIM) has an
average of 0.03, its range spans from a mere 0.007 to a high of 0.09. This stark variability is
reflective of the diverse financial landscapes and strategic decision-making processes different
institutions inhabit. Such disparities could potentially stem from varying degrees of exposure to
market risks or differing strategic lending approaches.
The Cost to Income Ratio (CIR), presenting an average of 0.48, also displays a widespread, ranging
from 0.227 to 0.87. Such variance offers a snapshot into the wide range of operational efficiencies
or inefficiencies across institutions. Some organizations might be leveraging technological
advancements to curb operational expenses, while others could be facing heightened costs due to
legacy systems or expansive endeavors.
The Digital Transformation Index (DTI), with its mean value of 0.21, peaks at 0.6, suggesting that
while many entities are making strides in digital adoption, some are on the forefront, pushing the
boundaries of what's feasible. This pioneering spirit is further echoed in the Business
Transformation Index (BI). Although its average sits at 0.4, it stretches up to a notable 0.81,
signifying certain institutions are considerably more invested in digitalizing their core business
operations. On the contrary, the Management Transformation Index (MI) offers a subdued
maximum of 0.4, subtly indicating a potential reluctance or slower pace at the top managerial tiers
to embrace full-scale digital transformation. Besides, the StrategyTransformation Index (SI) paints

24
an intriguing picture. Despite having an average of 0.18, its spread from 0 to a perfect 1 showcases
the dichotomy between institutions just beginning their strategic digital transformation and those
that have fully embraced the shift.
The Non-Performing Loan Ratio (NPL), though having a modest average, reaches an alarming
peak of 0.24. Such a figure might hint at certain institutions navigating tumultuous waters, with a
segment of their lending portfolio under stress. On the liquidity front, the Loan to Deposit (LD)
ratio, while averaging 0.94, touches a high of 1.43, hinting at possible aggressive lending strategies
by some institutions.
4.3 Regression Analysis
The findings from the regression analysis provide insightful evidence on the influence of digital
transformation metrics on the financial performance of banks. Specifically, the regression was
performed to determine the relationship between the Net Interest Margin (NIM) and the Cost to
Income Ratio (CIR) with the Digital Transformation Index (DTI), Strategy Transformation Index
(SI), Business Transformation Index (BI), and Management Transformation Index (MI), while
controlling for Total Asset (SIZE), Equity to Asset (EA), Loan to Deposit (LD), Loan to Asset
(LA), and the Independence proportion of board (IND).
The relationship between the various transformation indices and the Cost to Income Ratio (CIR)
offers some intriguing insights into the dynamics of digital transformation within the banking
sector. The positive beta value associated with the Digital Transformation Index (DTI) suggests a
potential direct correlation between the extent of digital transformation and banks' operational
efficiency. As banks intensify their digital transformation efforts, they might face elevated
operational costs. This could be attributed to initial investments in technology, the integration of
new digital platforms, or possible redundancy costs. However, it's essential to note that the
statistical significance of DTI's association with CIR was not within traditional thresholds,
prompting further exploration.
In stark contrast, the Business Transformation Index (BT) exhibited a significant positive
relationship with CIR at the 0.05 level. This seems to indicate that as banks dive deeper into the
digitalization of their core business operations, they are likely to witness a rise in operational costs.
The transformative shift towards digital platforms and processes in core business areas might be
accompanied by steep learning curves, necessitating investments in training, technology, and
perhaps even in restructuring initiatives. The negative beta value for the Management
Transformation Index (MT) presents an interesting facet of the digital transformation narrative. As
management leans more into the digital realm, the bank might observe improved operational
efficiency, potentially leading to a decline in CIR. This could point towards the potential
advantages of digitized management processes, from data-driven decision-making to more
streamlined managerial tasks. Yet, the absence of strong statistical significance in this relationship
suggests that the influence of management's digital transformation on operational costs might be
more complex than a direct linear association. The Strategy Transformation Index (ST) showcases
another dimension of the banking sector's digital adaptation efforts. Its beta value, albeit smaller
in magnitude, points to a subtle relationship between strategic shifts toward digital mechanisms

25
and the operational efficiency measured by CIR. While this relationship was not statistically strong
in the traditional sense, its presence suggests that a bank's overarching digital strategy might have
nuanced impacts on its cost structures.
Among the control variables, the significant negative relationship of SIZE with CIR warrants
attention. Larger banks might harness economies of scale, diffusing their fixed costs over a broader
operational base. The Equity to Asset (EA) and Loan to Asset (LA) ratios both have meaningful
interactions with CIR, reflecting the intricate interplay between a bank's capital structure, lending
behavior, and its operational costs. A higher equity base might insinuate a more cautious financial
approach, while an elevated loan portfolio relative to assets could signify a more aggressive
lending stance, each influencing the bank's cost dynamics in their own way.
Table 7: Regression results
Variable CIR CIR CIR CIR NIM NIM NIM NIM
const 0.95*** 0.95*** 0.93*** 0.95*** -0.03*** -0.03*** -0.03*** -0.03***
DTI 0.07 0.01
ST 0.03 -0.001
BT 0.15** 0.01**
MT -0.07 -0.01
SIZE -0.40*** -0.40*** -0.43*** -0.38*** 0.02*** 0.02*** 0.02*** 0.02***
EA -1.28*** -1.25*** -1.31*** -1.18*** 0.17*** 0.18*** 0.17*** 0.18***
LA 0.17** 0.17** 0.19** 0.16* -0.02** -0.02** -0.01** -0.02**
LD -0.29*** -0.28*** -0.31*** -0.27*** 0.05*** 0.05*** 0.04*** 0.05***
IND -0.11 -0.11 -0.11 -0.10 0.02* 0.02* 0.02* 0.02*
R-squared 0.59 0.59 0.60 0.59 0.64 0.64 0.65 0.64
***, **, * indicate that the relationship is significant at 1%, 5% and 10% respectively
Source: Author
For models using Net Interest Margin (NIM) as dependence variable, starting with the Digital
Transformation Index (DTI), the positive yet minuscule beta value suggests a mild direct
relationship between digital transformation endeavors and NIM. It appears that as banks ramp up
their digital transformation efforts, there could be a slight uptick in their net interest earnings. This
might reflect the efficiency gains achieved through digital channels, potentially facilitating faster
loan processing, more accurate risk assessment, or even better interest rate management. However,
the exact magnitude and significance of this relationship need further exploration.
The Strategy Transformation Index (ST) relationship with NIM is intriguing, albeit subtle. The
almost negligible negative beta implies a very soft inverse relationship. As banks pivot their
strategic outlook to align more with digital aspirations, there might be an initial dip in interest
margins. This could stem from transitional challenges, increased competition in the digital realm,
or even the integration of new strategic avenues that initially offer thinner margins. A more

26
pronounced relationship is evident with the Business Transformation Index (BT). The positive
beta, significant at the 0.05 level, underscores that banks pushing the envelope in digitizing their
core business operations might witness a marked boost in their net interest margins. This could be
attributed to a plethora of factors: superior customer targeting, tailored loan products using data
analytics, or perhaps more efficient capital allocation through digital tools. Contrastingly, the
Management Transformation Index (MT) presented a negative beta with NIM, suggesting that as
management processes and strategies integrate more digital tools and perspectives, there might be
a mild pressure on the net interest margins. The reasons could range from short-term misalignments
between digital strategy and market realities, a sharper focus on non-interest income sources, or
even potential teething troubles as management grapples with new digital paradigms.
Turning to the control variables, SIZE's positive relationship with NIM is intriguing. Larger banks,
possibly due to their diverse product offerings or better negotiation powers, might command better
interest spreads. The Equity to Asset (EA) ratio's positive significance highlights the profitability
implications of a strong capital base. In contrast, both Loan to Asset (LA) and Loan to Deposit
(LD) ratios, when viewed in tandem, shed light on the lending behaviors and their cascading effects
on interest margins. A nuanced understanding would involve dissecting the quality, diversity, and
tenure of the loan portfolios underpinning these metrics.
Furthermore, The R-squared values across models ranged from 0.59 to 0.65, indicating a good fit
and explaining a substantial variation in the dependent variables by the independent and control
factors. This range is considered notably high, especially in the realm of social sciences and
economic research. Such values suggest that between 59% to 65% of the variability in the
dependent metrics (CIR and NIM) is systematically explained by the independent and control
variables incorporated in the models.
5 Conclusions & Recommendations
5.2 Conclusions
The transformative wave of digitalization has not only redefined industries but also profoundly
influenced the very fundamentals of banking. As the nexus between digital transformation and
bank performance comes under academic scrutiny, this study unearths a landscape interspersed
with both opportunities and challenges. This research embarked on an explorative journey to
comprehend this intricate relationship, drawing from diverse empirical studies, and employing
rigorous methodologies. As venturing into the conclusion of our analysis, the authors aim to
consolidate our findings, drawing parallels with previous research, and highlighting where the
results align or diverge from established narratives. The ensuing conclusions not only shed light
on the present state of digital transformation within the banking sector but also provide directional
insights for future endeavors in this realm.
First, the analysis of the Digital Transformation Index (DTI) in regression models revealed an
interesting dynamic: while there is an observed association between the depth of digital
transformation and operational costs, this relationship was not statistically significant due to a high
p-value. Such an outcome suggests that the immediate impact of digital transformation on

27
operational costs might be more nuanced than initially presumed. This nuanced relationship
mirrors Qi and Cai (2020) findings where various dimensions of digital transformation may
neutralize each other's effects. The upfront costs observed in our models, potentially resulting from
initial investments in digital platforms, might be offset by long-term operational efficiencies or
other latent benefits. Contrastingly, the research by Kolodiziev et al. (2021) highlighted the
enhanced competitiveness of banks embracing digital innovations. Although this study did not
conclusively establish a significant relationship between DTI and operational costs, it does resonate
with the idea that the digital transition might involve complex cost-benefit dynamics that manifest
over extended timelines.
For Strategy Transformation (ST), the results from the regression models underscore a pivotal
revelation: strategic transformation, as evidenced by the frequency of digital technology-related
terms in annual reports, is significantly associated with enhanced bank performance. These
findings gain prominence when juxtaposed with the prevailing academic discourse. The salient
association identified in this research mirrors the findings of Xie and Wang (2023), who also
delineated the instrumental role of a strategic focus on digital technology. Their methodology of
using keyword frequency in annual reports emerges as an astute measure for gauging the depth of
a bank's digital strategy, a conclusion further corroborated by the results presented here. However,
as highlighted by scholars like Qi and Cai (2020) and Wu et al. (2021), while the keyword
frequency approach is insightful, it is not devoid of potential pitfalls. Subjective biases in term
selection or the possibility of overlooking emergent technology terminologies can be a concern.
Still, the outcomes of this study, in tandem with Xie and Wang (2023) work, cement the idea that
an authentic strategic transformation, underpinned by a clear digital emphasis, can significantly
influence bank performance. In essence, the findings of this research spotlight strategic
transformation not merely as a supplementary facet, but as a central driver in the digital banking
narrative, guiding performance outcomes and molding competitive landscapes. This underscores
the imperative for banks to not only recognize but also deeply integrate digital strategies at their
core, leveraging the benefits of the ongoing digital revolution.
Next, for Business Transformation (BT): The regression results presented an intriguing outcome
concerning business transformation and its relationship with bank performance. Even though a
positive correlation was observed, suggesting that digital channels, products, and R&D innovation
might play a role in enhancing banking operations, this relationship wasn't statistically significant
in our models. This nuance offers an interesting parallel to the insights shared by (Van Thuy, 2021).
While Van Thuy (2021) accentuated the profound influence of digital incorporation into financial
services, especially in the Vietnamese context, it's evident from our findings that the strength of
this relationship varies across different contexts or may be impacted by other mediating factors not
captured in our study. Drawing from the approach of Xie and Wang (2023), this research attempted
to holistically gauge the facets of business transformation. Nevertheless, the results underscore the
need for a more granular understanding and perhaps, an exploration of specific intervening
variables that could bolster this relationship's significance.
Finally, for Management Transformation (MT): Diving into the realm of management
transformation, the regression results paint a multifaceted picture. While there's an undeniable

28
emphasis on organizational restructuring and the absorption of digital proficiency, the statistical
significance of this relationship with bank performance remains elusive in our models. Such an
outcome reverberates with the assertions of Liu et al. (2021) and Qi and Cai (2020), hinting at the
intricate balance banks must strike. On one hand, introducing digital mechanisms within
management processes can pave the way for heightened operational dexterity. On the other hand,
these digital incursions can also usher in a suite of challenges, from change management hurdles
to potential operational bottlenecks. The findings from this research indicate that a mere shift
towards digital avenues in management doesn't guarantee enhanced performance. It's perhaps the
strategic synthesis of these technologies, as reflected in the importance of collaborations and
partnerships with tech stalwarts, that holds the key. This observation underscores the imperative
for banks to not only integrate digital tools but to do so with a coherent and well-charted strategy.
Moreover, this research's findings illuminate the intricate dynamics of digital transformation,
echoing sentiments previously voiced by Hajli et al. (2015). Digital transformation, though
universally acknowledged as a catalyst for progress, exhibits varying degrees of impact across
different institutions. While numerous banks may be poised to harness the potential of digital
transformation as a lever to amplify performance, the resulting advantages aren't uniform. The
ultimate benefits realized are intricately interwoven with several factors. Firstly, the bank's unique
operational and market context plays a pivotal role in dictating the trajectory and intensity of the
gains. Secondly, the methodologies and approaches deployed for the integration of digital elements
into the banking ecosystem can make a significant difference. Some methods may lead to optimal
results, while others might falter due to unanticipated challenges or operational misalignments.
Lastly, the dimensions of transformation a bank emphasizes, whether it's strategic, business, or
management transformation, can influence the magnitude and nature of the outcomes. This
nuanced landscape reinforces the notion that digital transformation isn't a one-size-fits-all solution
but rather a tailored journey requiring introspection, strategy, and adaptability.
In summary, while our results largely agree with the broader narratives presented in the empirical
research, such as the works of Xie and Wang (2023), Van Thuy (2021), and Liu et al. (2021), they
also bring to light the nuances and intricacies of the digital transformation journey. The findings
emphasize the need for banks to adopt a holistic, well-strategized approach, ensuring that their
digital transformation efforts are well-aligned with their overarching organizational goals and
dynamics.
5.3 Recommendations
Based on the findings and insights of this research, it's clear that the digital transformation journey
within the banking sector is complex and multi-dimensional. To effectively maneuver through this
terrain and optimize benefits, the subsequent recommendations are presented. They are tailored for
investors, managers, and regulatory bodies, helping them refine their strategies in the dynamic
digital banking environment.
For investors: The findings of this research underscore the nuanced approach required to
understand the dynamics of digital transformation within the banking sector. While a bank's
commitment to digital initiatives might be evident through financial investments, it's essential for

29
investors to delve deeper. Evaluating how a bank balances and integrates different transformation
dimensions – strategy, business, and management – can provide better insights into the institution's
long-term viability in a digital age. Moreover, the mere adoption of digital tools and practices isn't
a direct indicator of success. Banks that holistically incorporate digital innovations into their
foundational strategies, core services, and organizational structures are more likely to realize
sustainable benefits. Consequently, investors should prioritize institutions that demonstrate a clear
vision, coupled with tactical execution, in their digital transformation endeavors.
For managers: The findings of this research highlight that the mere act of digital adoption doesn't
equate to success; instead, the integration and alignment of digital initiatives with the bank's core
objectives stand out as pivotal. Managers have a dual role to play – as technologists and cultural
ambassadors. Firstly, on the technology front, it's crucial to recognize that while digital tools can
enhance operations, their implementation needs to be strategic. This means that any digital
initiative, be it the adoption of new software or the creation of a digital channel, should map back
to the broader goals of the bank. Secondly, as cultural ambassadors, managers need to ensure the
organization is adaptive to change. In the digital age, change is constant, and resistance can be a
significant roadblock. Managers should prioritize cultivating a culture that values continuous
learning. Regular workshops, training sessions, and feedback loops can help in ensuring that the
staff is not only equipped with the latest digital skills but is also mentally agile and open to
embracing new ways of working. Moreover, in the era of partnerships and collaborations,
managers should exercise discernment. Engaging with tech entities should not just be about
leveraging their technological prowess but should also focus on ensuring that these collaborations
are symbiotic, aligning with the bank's vision and enhancing customer experience. Simply put,
every partnership should make strategic sense and not just be a result of jumping on the digital
bandwagon.
For related government agencies: This research underscores the transformative potential of digital
integration within the banking sector. However, it also illuminates the intricate challenges that
intertwine with such endeavors. To strike a balance between innovation and security, government
bodies should consider crafting a dual-pronged regulatory approach. Firstly, there should be
provisions to foster innovation by creating a conducive environment. This can be achieved through
measures such as offering incentives, facilitating research and development grants, or even
establishing digital incubation hubs where new banking technologies can be piloted and refined.
On the flip side, as digital frontiers expand, so do the realms of vulnerabilities. Thus, it's imperative
for regulatory frameworks to integrate robust cybersecurity protocols. These not only act as a
deterrent against potential cyber threats but also instill trust among consumers, ensuring they
remain confident in the digital banking ecosystem. Lastly, the velocity at which the digital world
evolves is staggering. Hence, government agencies should adopt a dynamic approach to
regulations, ensuring periodic reviews and updates, to ensure alignment with the latest
technological trends and emerging challenges. This adaptability will ensure that the banking sector
remains both innovative and secure as it marches into the digital future.

30
5.4 Limitations & Further Research
This research, while shedding light on the intricacies of digital transformation in the banking sector,
does have certain limitations. Primarily, the scope was concentrated on the banking sector,
potentially overlooking insights from other financial service industries. Moreover, the study's
geographical focus might limit the generalizability of findings across different cultural or economic
contexts. A notable methodological constraint is the reliance on the frequency of digital
technology-related terms in annual reports, which, although insightful, may not provide a holistic
picture of a bank's digital endeavors.
Besides, numerous paths open for additional investigation. Across-industry analysis encompassing
sectors like insurance or asset management might offer a richer understanding of the digital
transformation landscape. It would also be enlightening to undertake a comparative global study,
dissecting digital transformation journeys across various banking landscapes, thereby uncovering
best practices and unique regional challenges. The rapid pace of technological evolution
necessitates longitudinal studies, tracking the continuous progress of banks in their digital
transformation trajectories. Furthermore, embracing qualitative methodologies could unearth
deeper organizational motivations and challenges associated with digital shifts. As the digital
horizon expands with emerging technologies and changing consumer expectations, research should
stay abreast, continually diving into newer paradigms and perspectives.
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